Multi-Energy CT Image Noise Reduction via Conglomerate Data
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Solution Overview
Problem
Spectral CT imaging faces a tradeoff between material differentiation and image noise due to the selection of energy bins, where narrow bins improve signal but increase noise, and increasing photons to reduce noise raises patient dose concerns.
Innovation Solution
A method and system that create an energy series of images by acquiring and weighting data from multiple energy bins, forming a conglomerate data set to reconstruct images with reduced noise and improved signal-to-noise ratio, allowing optimal selection of energy bins without increasing noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If narrow energy bins are used to improve material differentiation, then signal quality is improved, but image noise increases
Solution Approach 1:
The patent segments the energy spectrum into multiple energy bins (first energy bin, second energy bin, etc.) to enable material differentiation. By dividing the continuous energy spectrum into discrete segments, the system can analyze attenuation characteristics at different energy levels independently, improving the ability to distinguish between different materials based on their energy-dependent attenuation properties.
Solution Approach 2:
The patent merges data from multiple energy bins to create a conglomerate data set. The first energy-selective data set from the first energy bin is combined with the second energy-selective data set from the second energy bin, and potentially additional data sets from other energy bins. This merging allows the system to leverage information from multiple energy ranges to improve overall image quality and reduce noise while maintaining material differentiation capabilities.
2Reliability
If photons are increased to reduce image noise, then image quality is improved, but patient radiation dose increases
Solution Approach 1:
The patent changes the energy parameter by selecting multiple different energy bins for data acquisition. Instead of using a single energy level or broadly distributed photons, the system acquires data at specific energy ranges (first energy bin, second energy bin, etc.). This parameter change allows the system to achieve better signal-to-noise ratios through intelligent energy selection and data combination, reducing the need to increase overall photon flux and thereby limiting patient radiation exposure.
Solution Approach 2:
The patent replaces the mechanical approach of simply increasing photon quantity with a data processing approach. Instead of mechanically delivering more radiation to reduce noise, the system uses computational methods to weight and combine data from multiple energy bins. The weighting process substitutes for brute-force photon increase, achieving noise reduction through information optimization rather than radiation dose escalation.
3Loss of information
If multiple energy bins are selected for material differentiation, then spectral information is improved, but data complexity increases
Solution Approach 1:
The patent introduces a conglomerate data set as an intermediary between the multiple energy-selective data sets and the final images. The weighting process acts as a mediator that harmonizes data from different energy bins, combining them in a systematic way that preserves spectral information while managing complexity. This intermediary structure provides a clear framework for integrating multi-energy data without overwhelming computational complexity.
Solution Approach 2:
The patent performs preliminary weighting of energy-selective data sets before final image reconstruction. By pre-weighting the data from each energy bin based on their respective qualities and characteristics, the system prepares the data in advance for combination. This preliminary action simplifies the subsequent reconstruction process, as the weighting decisions are already made, reducing the computational burden during final image generation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces image noise to conventional CT levels, maintaining energy-specific information and allowing flexible energy bin selection for improved material differentiation without increasing patient dose or noise.
Implementation Method 1
an x-ray source projects a fan or cone shaped beam... The x-ray beam passes through the object being imaged... and impinges upon an array of radiation detectors. The intensity of the transmitted radiation is dependent upon the attenuation of the x-ray beam by the object
Implementation Method 2
The intensity of the transmitted radiation is dependent upon the attenuation of the x-ray beam by the object and each detector produces a separate electrical signal that is a measurement of the beam attenuation
Data Source
AI summary
A method for creating an energy series of images acquired using a multi-energy computed tomography (CT) imaging system having a plurality of energy bins includes acquiring, with the multi-energy CT imaging system, a series of energy data sets, where each energy data set is associated with at least one of the energy bins. The method includes producing a conglomerate image using at least a plurality of the energy data sets and, using the conglomerate image, reconstructing an energy series of images, each image in the energy series of images corresponding to at least one of the energy data sets.